Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations336776
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory102.2 MiB
Average record size in memory318.3 B

Variable types

Categorical5
Numeric14
Text1
DateTime1

Alerts

year has constant value "2013" Constant
dest has a high cardinality: 105 distinct values High cardinality
air_time is highly overall correlated with distance and 1 other fieldsHigh correlation
arr_delay is highly overall correlated with dep_delayHigh correlation
arr_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
carrier is highly overall correlated with originHigh correlation
dep_delay is highly overall correlated with arr_delayHigh correlation
dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
distance is highly overall correlated with air_time and 1 other fieldsHigh correlation
hour is highly overall correlated with arr_time and 3 other fieldsHigh correlation
month is highly overall correlated with month_nameHigh correlation
month_name is highly overall correlated with monthHigh correlation
origin is highly overall correlated with carrierHigh correlation
sched_arr_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
sched_dep_time is highly overall correlated with arr_time and 3 other fieldsHigh correlation
speed is highly overall correlated with air_time and 1 other fieldsHigh correlation
dep_delay has 16514 (4.9%) zeros Zeros
arr_delay has 5409 (1.6%) zeros Zeros
minute has 60696 (18.0%) zeros Zeros

Reproduction

Analysis started2025-05-24 03:45:54.573019
Analysis finished2025-05-24 03:47:00.278368
Duration1 minute and 5.71 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 MiB
2013
336776 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1347104
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 336776
100.0%

Length

2025-05-24T09:17:00.387922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T09:17:00.498248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2013 336776
100.0%

Most occurring characters

ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1347104
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1347104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1347104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 336776
25.0%
0 336776
25.0%
1 336776
25.0%
3 336776
25.0%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.54851
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:00.631627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4144572
Coefficient of variation (CV)0.52140979
Kurtosis-1.1869501
Mean6.54851
Median Absolute Deviation (MAD)3
Skewness-0.013399885
Sum2205381
Variance11.658518
MonotonicityNot monotonic
2025-05-24T09:17:00.768229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 29425
8.7%
8 29327
8.7%
10 28889
8.6%
3 28834
8.6%
5 28796
8.6%
4 28330
8.4%
6 28243
8.4%
12 28135
8.4%
9 27574
8.2%
11 27268
8.1%
Other values (2) 51955
15.4%
ValueCountFrequency (%)
1 27004
8.0%
2 24951
7.4%
3 28834
8.6%
4 28330
8.4%
5 28796
8.6%
6 28243
8.4%
7 29425
8.7%
8 29327
8.7%
9 27574
8.2%
10 28889
8.6%
ValueCountFrequency (%)
12 28135
8.4%
11 27268
8.1%
10 28889
8.6%
9 27574
8.2%
8 29327
8.7%
7 29425
8.7%
6 28243
8.4%
5 28796
8.6%
4 28330
8.4%
3 28834
8.6%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.710787
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:00.885744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7686071
Coefficient of variation (CV)0.55812653
Kurtosis-1.1859454
Mean15.710787
Median Absolute Deviation (MAD)8
Skewness0.0077444993
Sum5291016
Variance76.888471
MonotonicityNot monotonic
2025-05-24T09:17:01.053159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18 11399
 
3.4%
11 11359
 
3.4%
22 11345
 
3.4%
15 11317
 
3.4%
8 11271
 
3.3%
10 11227
 
3.3%
17 11222
 
3.3%
3 11211
 
3.3%
21 11141
 
3.3%
20 11111
 
3.3%
Other values (21) 224173
66.6%
ValueCountFrequency (%)
1 11036
3.3%
2 10808
3.2%
3 11211
3.3%
4 11059
3.3%
5 10858
3.2%
6 11059
3.3%
7 10985
3.3%
8 11271
3.3%
9 10857
3.2%
10 11227
3.3%
ValueCountFrequency (%)
31 6190
1.8%
30 10289
3.1%
29 10039
3.0%
28 10773
3.2%
27 11084
3.3%
26 10883
3.2%
25 11097
3.3%
24 11041
3.3%
23 10966
3.3%
22 11345
3.4%

dep_time
Real number (ℝ)

High correlation 

Distinct1319
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1349.1099
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:01.195400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile625
Q1915
median1349.1099
Q31737
95-th percentile2110
Maximum2400
Range2399
Interquartile range (IQR)822

Descriptive statistics

Standard deviation482.2603
Coefficient of variation (CV)0.35746553
Kurtosis-1.0402831
Mean1349.1099
Median Absolute Deviation (MAD)408.89005
Skewness-0.025052395
Sum4.5434785 × 108
Variance232575
MonotonicityNot monotonic
2025-05-24T09:17:01.368308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1349.109947 8255
 
2.5%
555 834
 
0.2%
755 820
 
0.2%
556 818
 
0.2%
557 799
 
0.2%
655 798
 
0.2%
1455 774
 
0.2%
1454 769
 
0.2%
654 751
 
0.2%
855 743
 
0.2%
Other values (1309) 321415
95.4%
ValueCountFrequency (%)
1 25
< 0.1%
2 35
< 0.1%
3 26
< 0.1%
4 26
< 0.1%
5 21
< 0.1%
6 22
< 0.1%
7 22
< 0.1%
8 23
< 0.1%
9 28
< 0.1%
10 22
< 0.1%
ValueCountFrequency (%)
2400 29
 
< 0.1%
2359 55
< 0.1%
2358 76
< 0.1%
2357 74
< 0.1%
2356 74
< 0.1%
2355 82
< 0.1%
2354 69
< 0.1%
2353 68
< 0.1%
2352 68
< 0.1%
2351 57
< 0.1%

sched_dep_time
Real number (ℝ)

High correlation 

Distinct1021
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344.2548
Minimum106
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:01.618432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile630
Q1906
median1359
Q31729
95-th percentile2050
Maximum2359
Range2253
Interquartile range (IQR)823

Descriptive statistics

Standard deviation467.33576
Coefficient of variation (CV)0.34765414
Kurtosis-1.1979031
Mean1344.2548
Median Absolute Deviation (MAD)414
Skewness-0.0058580829
Sum4.5271277 × 108
Variance218402.71
MonotonicityNot monotonic
2025-05-24T09:17:01.840335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 7016
 
2.1%
700 4900
 
1.5%
630 4770
 
1.4%
900 4766
 
1.4%
1200 4624
 
1.4%
1700 4526
 
1.3%
1600 4098
 
1.2%
800 3926
 
1.2%
1300 3689
 
1.1%
1900 3653
 
1.1%
Other values (1011) 290808
86.4%
ValueCountFrequency (%)
106 1
 
< 0.1%
500 341
0.1%
501 1
 
< 0.1%
505 2
 
< 0.1%
510 5
 
< 0.1%
515 208
0.1%
516 4
 
< 0.1%
517 28
 
< 0.1%
520 7
 
< 0.1%
525 37
 
< 0.1%
ValueCountFrequency (%)
2359 828
0.2%
2358 44
 
< 0.1%
2355 73
 
< 0.1%
2352 16
 
< 0.1%
2345 1
 
< 0.1%
2339 1
 
< 0.1%
2330 14
 
< 0.1%
2315 1
 
< 0.1%
2305 61
 
< 0.1%
2300 22
 
< 0.1%

dep_delay
Real number (ℝ)

High correlation  Zeros 

Distinct528
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.63907
Minimum-43
Maximum1301
Zeros16514
Zeros (%)4.9%
Negative183575
Negative (%)54.5%
Memory size2.6 MiB
2025-05-24T09:17:02.550356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-43
5-th percentile-9
Q1-5
median-1
Q312.63907
95-th percentile87
Maximum1301
Range1344
Interquartile range (IQR)17.63907

Descriptive statistics

Standard deviation39.714191
Coefficient of variation (CV)3.1421766
Kurtosis45.129851
Mean12.63907
Median Absolute Deviation (MAD)5
Skewness4.8625042
Sum4256535.5
Variance1577.2169
MonotonicityNot monotonic
2025-05-24T09:17:02.738307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 24821
 
7.4%
-4 24619
 
7.3%
-3 24218
 
7.2%
-2 21516
 
6.4%
-6 20701
 
6.1%
-1 18813
 
5.6%
-7 16752
 
5.0%
0 16514
 
4.9%
-8 11791
 
3.5%
12.63907026 8255
 
2.5%
Other values (518) 148776
44.2%
ValueCountFrequency (%)
-43 1
 
< 0.1%
-33 1
 
< 0.1%
-32 1
 
< 0.1%
-30 1
 
< 0.1%
-27 1
 
< 0.1%
-26 1
 
< 0.1%
-25 2
 
< 0.1%
-24 4
 
< 0.1%
-23 6
< 0.1%
-22 11
< 0.1%
ValueCountFrequency (%)
1301 1
< 0.1%
1137 1
< 0.1%
1126 1
< 0.1%
1014 1
< 0.1%
1005 1
< 0.1%
960 1
< 0.1%
911 1
< 0.1%
899 1
< 0.1%
898 1
< 0.1%
896 1
< 0.1%

arr_time
Real number (ℝ)

High correlation 

Distinct1412
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1502.055
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:02.900590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile739
Q11110
median1518
Q31934
95-th percentile2247
Maximum2400
Range2399
Interquartile range (IQR)824

Descriptive statistics

Standard deviation526.32066
Coefficient of variation (CV)0.35040039
Kurtosis-0.11807347
Mean1502.055
Median Absolute Deviation (MAD)412
Skewness-0.47399068
Sum5.0585607 × 108
Variance277013.43
MonotonicityNot monotonic
2025-05-24T09:17:03.153833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1502.054999 8713
 
2.6%
1008 485
 
0.1%
1013 484
 
0.1%
1015 479
 
0.1%
1012 464
 
0.1%
1005 460
 
0.1%
1016 459
 
0.1%
1006 459
 
0.1%
1011 457
 
0.1%
1007 456
 
0.1%
Other values (1402) 323860
96.2%
ValueCountFrequency (%)
1 201
0.1%
2 164
< 0.1%
3 174
0.1%
4 173
0.1%
5 206
0.1%
6 148
< 0.1%
7 170
0.1%
8 147
< 0.1%
9 140
< 0.1%
10 178
0.1%
ValueCountFrequency (%)
2400 150
< 0.1%
2359 222
0.1%
2358 189
0.1%
2357 207
0.1%
2356 202
0.1%
2355 206
0.1%
2354 195
0.1%
2353 182
0.1%
2352 193
0.1%
2351 216
0.1%

sched_arr_time
Real number (ℝ)

High correlation 

Distinct1163
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1536.3802
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:03.368007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile815
Q11124
median1556
Q31945
95-th percentile2246
Maximum2359
Range2358
Interquartile range (IQR)821

Descriptive statistics

Standard deviation497.45714
Coefficient of variation (CV)0.32378518
Kurtosis-0.38224779
Mean1536.3802
Median Absolute Deviation (MAD)417
Skewness-0.35313807
Sum5.1741598 × 108
Variance247463.61
MonotonicityNot monotonic
2025-05-24T09:17:03.516685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025 1324
 
0.4%
2015 1234
 
0.4%
1110 1198
 
0.4%
1115 1193
 
0.4%
1235 1133
 
0.3%
2359 1121
 
0.3%
1815 1111
 
0.3%
1015 1080
 
0.3%
1645 1079
 
0.3%
1220 1073
 
0.3%
Other values (1153) 325230
96.6%
ValueCountFrequency (%)
1 243
0.1%
2 95
 
< 0.1%
3 159
< 0.1%
4 107
< 0.1%
5 82
 
< 0.1%
6 19
 
< 0.1%
7 85
 
< 0.1%
8 154
< 0.1%
9 55
 
< 0.1%
10 72
 
< 0.1%
ValueCountFrequency (%)
2359 1121
0.3%
2358 483
0.1%
2357 349
 
0.1%
2356 468
0.1%
2355 335
 
0.1%
2354 384
 
0.1%
2353 263
 
0.1%
2352 47
 
< 0.1%
2351 140
 
< 0.1%
2350 105
 
< 0.1%

arr_delay
Real number (ℝ)

High correlation  Zeros 

Distinct578
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8953768
Minimum-86
Maximum1272
Zeros5409
Zeros (%)1.6%
Negative188933
Negative (%)56.1%
Memory size2.6 MiB
2025-05-24T09:17:03.708236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-32
Q1-16
median-4
Q313
95-th percentile89
Maximum1272
Range1358
Interquartile range (IQR)29

Descriptive statistics

Standard deviation44.003969
Coefficient of variation (CV)6.3816628
Kurtosis30.161583
Mean6.8953768
Median Absolute Deviation (MAD)14
Skewness3.7699729
Sum2322197.4
Variance1936.3493
MonotonicityNot monotonic
2025-05-24T09:17:03.877949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.895376757 9430
 
2.8%
-13 7177
 
2.1%
-10 7088
 
2.1%
-12 7046
 
2.1%
-14 6975
 
2.1%
-11 6863
 
2.0%
-9 6815
 
2.0%
-15 6796
 
2.0%
-7 6677
 
2.0%
-17 6668
 
2.0%
Other values (568) 265241
78.8%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-79 1
 
< 0.1%
-75 2
 
< 0.1%
-74 1
 
< 0.1%
-73 1
 
< 0.1%
-71 3
 
< 0.1%
-70 8
< 0.1%
-69 7
< 0.1%
-68 12
< 0.1%
-67 7
< 0.1%
ValueCountFrequency (%)
1272 1
< 0.1%
1127 1
< 0.1%
1109 1
< 0.1%
1007 1
< 0.1%
989 1
< 0.1%
931 1
< 0.1%
915 1
< 0.1%
895 1
< 0.1%
878 1
< 0.1%
875 1
< 0.1%

carrier
Categorical

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size330.5 KiB
UA
58665 
B6
54635 
EV
54173 
DL
48110 
AA
32729 
Other values (11)
88464 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters673552
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowUA
3rd rowAA
4th rowB6
5th rowDL

Common Values

ValueCountFrequency (%)
UA 58665
17.4%
B6 54635
16.2%
EV 54173
16.1%
DL 48110
14.3%
AA 32729
9.7%
MQ 26397
7.8%
US 20536
 
6.1%
9E 18460
 
5.5%
WN 12275
 
3.6%
VX 5162
 
1.5%
Other values (6) 5634
 
1.7%

Length

2025-05-24T09:17:04.050373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua 58665
17.4%
b6 54635
16.2%
ev 54173
16.1%
dl 48110
14.3%
aa 32729
9.7%
mq 26397
7.8%
us 20536
 
6.1%
9e 18460
 
5.5%
wn 12275
 
3.6%
vx 5162
 
1.5%
Other values (6) 5634
 
1.7%

Most occurring characters

ValueCountFrequency (%)
A 125179
18.6%
U 79201
11.8%
E 72633
10.8%
V 59936
8.9%
B 54635
8.1%
6 54635
8.1%
L 51370
7.6%
D 48110
 
7.1%
Q 26397
 
3.9%
M 26397
 
3.9%
Other values (9) 75059
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 599772
89.0%
Decimal Number 73780
 
11.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 125179
20.9%
U 79201
13.2%
E 72633
12.1%
V 59936
10.0%
B 54635
9.1%
L 51370
8.6%
D 48110
 
8.0%
Q 26397
 
4.4%
M 26397
 
4.4%
S 21250
 
3.5%
Other values (7) 34664
 
5.8%
Decimal Number
ValueCountFrequency (%)
6 54635
74.1%
9 19145
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 599772
89.0%
Common 73780
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 125179
20.9%
U 79201
13.2%
E 72633
12.1%
V 59936
10.0%
B 54635
9.1%
L 51370
8.6%
D 48110
 
8.0%
Q 26397
 
4.4%
M 26397
 
4.4%
S 21250
 
3.5%
Other values (7) 34664
 
5.8%
Common
ValueCountFrequency (%)
6 54635
74.1%
9 19145
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 673552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 125179
18.6%
U 79201
11.8%
E 72633
10.8%
V 59936
8.9%
B 54635
8.1%
6 54635
8.1%
L 51370
7.6%
D 48110
 
7.1%
Q 26397
 
3.9%
M 26397
 
3.9%
Other values (9) 75059
11.1%

flight
Real number (ℝ)

Distinct3844
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.9236
Minimum1
Maximum8500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:04.201456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile91
Q1553
median1496
Q33465
95-th percentile4695
Maximum8500
Range8499
Interquartile range (IQR)2912

Descriptive statistics

Standard deviation1632.4719
Coefficient of variation (CV)0.82785759
Kurtosis-0.84856068
Mean1971.9236
Median Absolute Deviation (MAD)1085
Skewness0.66160363
Sum6.6409655 × 108
Variance2664964.6
MonotonicityNot monotonic
2025-05-24T09:17:04.398175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 968
 
0.3%
27 898
 
0.3%
181 882
 
0.3%
301 871
 
0.3%
161 786
 
0.2%
695 782
 
0.2%
1109 716
 
0.2%
745 711
 
0.2%
359 709
 
0.2%
1 701
 
0.2%
Other values (3834) 328752
97.6%
ValueCountFrequency (%)
1 701
0.2%
2 51
 
< 0.1%
3 631
0.2%
4 393
0.1%
5 324
0.1%
6 210
 
0.1%
7 237
 
0.1%
8 236
 
0.1%
9 153
 
< 0.1%
10 61
 
< 0.1%
ValueCountFrequency (%)
8500 1
 
< 0.1%
6181 80
< 0.1%
6180 6
 
< 0.1%
6177 164
< 0.1%
6171 1
 
< 0.1%
6168 2
 
< 0.1%
6167 3
 
< 0.1%
6165 1
 
< 0.1%
6140 1
 
< 0.1%
6138 2
 
< 0.1%
Distinct4043
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size20.2 MiB
2025-05-24T09:17:04.930997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.995258
Min length5

Characters and Unicode

Total characters2019059
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)0.1%

Sample

1st rowN14228
2nd rowN24211
3rd rowN619AA
4th rowN804JB
5th rowN668DN
ValueCountFrequency (%)
n725mq 3087
 
0.9%
n722mq 513
 
0.2%
n723mq 507
 
0.2%
n711mq 486
 
0.1%
n713mq 483
 
0.1%
n258jb 427
 
0.1%
n298jb 407
 
0.1%
n353jb 404
 
0.1%
n351jb 402
 
0.1%
n735mq 396
 
0.1%
Other values (4033) 329664
97.9%
2025-05-24T09:17:05.591702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 363324
18.0%
3 149395
 
7.4%
1 145378
 
7.2%
5 138064
 
6.8%
A 118723
 
5.9%
7 117211
 
5.8%
2 112564
 
5.6%
9 106405
 
5.3%
4 102253
 
5.1%
6 101332
 
5.0%
Other values (24) 564410
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1125587
55.7%
Uppercase Letter 893472
44.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 363324
40.7%
A 118723
 
13.3%
B 69014
 
7.7%
J 66843
 
7.5%
U 46341
 
5.2%
W 35652
 
4.0%
Q 32249
 
3.6%
M 30261
 
3.4%
D 24010
 
2.7%
E 14851
 
1.7%
Other values (14) 92204
 
10.3%
Decimal Number
ValueCountFrequency (%)
3 149395
13.3%
1 145378
12.9%
5 138064
12.3%
7 117211
10.4%
2 112564
10.0%
9 106405
9.5%
4 102253
9.1%
6 101332
9.0%
8 84315
7.5%
0 68670
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1125587
55.7%
Latin 893472
44.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 363324
40.7%
A 118723
 
13.3%
B 69014
 
7.7%
J 66843
 
7.5%
U 46341
 
5.2%
W 35652
 
4.0%
Q 32249
 
3.6%
M 30261
 
3.4%
D 24010
 
2.7%
E 14851
 
1.7%
Other values (14) 92204
 
10.3%
Common
ValueCountFrequency (%)
3 149395
13.3%
1 145378
12.9%
5 138064
12.3%
7 117211
10.4%
2 112564
10.0%
9 106405
9.5%
4 102253
9.1%
6 101332
9.0%
8 84315
7.5%
0 68670
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2019059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 363324
18.0%
3 149395
 
7.4%
1 145378
 
7.2%
5 138064
 
6.8%
A 118723
 
5.9%
7 117211
 
5.8%
2 112564
 
5.6%
9 106405
 
5.3%
4 102253
 
5.1%
6 101332
 
5.0%
Other values (24) 564410
28.0%

origin
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size329.3 KiB
EWR
120835 
JFK
111279 
LGA
104662 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1010328
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowLGA
3rd rowJFK
4th rowJFK
5th rowLGA

Common Values

ValueCountFrequency (%)
EWR 120835
35.9%
JFK 111279
33.0%
LGA 104662
31.1%

Length

2025-05-24T09:17:05.802440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T09:17:05.948174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ewr 120835
35.9%
jfk 111279
33.0%
lga 104662
31.1%

Most occurring characters

ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1010328
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 120835
12.0%
W 120835
12.0%
R 120835
12.0%
J 111279
11.0%
F 111279
11.0%
K 111279
11.0%
L 104662
10.4%
G 104662
10.4%
A 104662
10.4%

dest
Categorical

High cardinality 

Distinct105
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.2 KiB
ORD
 
17283
ATL
 
17215
LAX
 
16174
BOS
 
15508
MCO
 
14082
Other values (100)
256514 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1010328
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIAH
2nd rowIAH
3rd rowMIA
4th rowBQN
5th rowATL

Common Values

ValueCountFrequency (%)
ORD 17283
 
5.1%
ATL 17215
 
5.1%
LAX 16174
 
4.8%
BOS 15508
 
4.6%
MCO 14082
 
4.2%
CLT 14064
 
4.2%
SFO 13331
 
4.0%
FLL 12055
 
3.6%
MIA 11728
 
3.5%
DCA 9705
 
2.9%
Other values (95) 195631
58.1%

Length

2025-05-24T09:17:06.113197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ord 17283
 
5.1%
atl 17215
 
5.1%
lax 16174
 
4.8%
bos 15508
 
4.6%
mco 14082
 
4.2%
clt 14064
 
4.2%
sfo 13331
 
4.0%
fll 12055
 
3.6%
mia 11728
 
3.5%
dca 9705
 
2.9%
Other values (95) 195631
58.1%

Most occurring characters

ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1010328
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 107619
 
10.7%
L 93530
 
9.3%
S 86165
 
8.5%
D 77868
 
7.7%
O 69436
 
6.9%
C 64362
 
6.4%
T 61733
 
6.1%
M 59156
 
5.9%
I 42485
 
4.2%
F 41782
 
4.1%
Other values (16) 306192
30.3%

air_time
Real number (ℝ)

High correlation 

Distinct510
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.68646
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:06.283579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q184
median132
Q3188
95-th percentile339
Maximum695
Range675
Interquartile range (IQR)104

Descriptive statistics

Standard deviation92.367314
Coefficient of variation (CV)0.61297686
Kurtosis0.97436221
Mean150.68646
Median Absolute Deviation (MAD)51
Skewness1.0860177
Sum50747583
Variance8531.7207
MonotonicityNot monotonic
2025-05-24T09:17:06.479414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150.6864602 9430
 
2.8%
42 2552
 
0.8%
43 2543
 
0.8%
41 2513
 
0.7%
45 2495
 
0.7%
40 2466
 
0.7%
44 2444
 
0.7%
39 2411
 
0.7%
47 2409
 
0.7%
46 2406
 
0.7%
Other values (500) 305107
90.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 14
 
< 0.1%
22 34
 
< 0.1%
23 82
 
< 0.1%
24 103
< 0.1%
25 124
< 0.1%
26 169
0.1%
27 147
< 0.1%
28 180
0.1%
29 209
0.1%
ValueCountFrequency (%)
695 1
< 0.1%
691 1
< 0.1%
686 2
< 0.1%
683 1
< 0.1%
679 1
< 0.1%
676 2
< 0.1%
675 1
< 0.1%
671 2
< 0.1%
669 1
< 0.1%
667 2
< 0.1%

distance
Real number (ℝ)

High correlation 

Distinct214
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1039.9126
Minimum17
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:06.723426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile199
Q1502
median872
Q31389
95-th percentile2475
Maximum4983
Range4966
Interquartile range (IQR)887

Descriptive statistics

Standard deviation733.23303
Coefficient of variation (CV)0.70509102
Kurtosis1.1936399
Mean1039.9126
Median Absolute Deviation (MAD)384
Skewness1.1286902
Sum3.5021761 × 108
Variance537630.68
MonotonicityNot monotonic
2025-05-24T09:17:06.898239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 11262
 
3.3%
762 10263
 
3.0%
733 8857
 
2.6%
2586 8204
 
2.4%
544 6168
 
1.8%
719 6100
 
1.8%
187 5898
 
1.8%
1096 5781
 
1.7%
2454 5695
 
1.7%
184 5504
 
1.6%
Other values (204) 263044
78.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
80 49
 
< 0.1%
94 976
 
0.3%
96 607
 
0.2%
116 443
 
0.1%
143 439
 
0.1%
160 376
 
0.1%
169 545
 
0.2%
173 221
 
0.1%
184 5504
1.6%
ValueCountFrequency (%)
4983 342
 
0.1%
4963 365
 
0.1%
3370 8
 
< 0.1%
2586 8204
2.4%
2576 312
 
0.1%
2569 329
 
0.1%
2565 5127
1.5%
2521 284
 
0.1%
2475 11262
3.3%
2465 1039
 
0.3%

hour
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.180247
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:07.087981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median13
Q317
95-th percentile20
Maximum23
Range22
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6613157
Coefficient of variation (CV)0.3536592
Kurtosis-1.2064161
Mean13.180247
Median Absolute Deviation (MAD)4
Skewness-0.00054265178
Sum4438791
Variance21.727864
MonotonicityNot monotonic
2025-05-24T09:17:07.178034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8 27242
 
8.1%
6 25951
 
7.7%
17 24426
 
7.3%
15 23888
 
7.1%
16 23002
 
6.8%
7 22821
 
6.8%
18 21783
 
6.5%
14 21706
 
6.4%
19 21441
 
6.4%
9 20312
 
6.0%
Other values (10) 104204
30.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
5 1953
 
0.6%
6 25951
7.7%
7 22821
6.8%
8 27242
8.1%
9 20312
6.0%
10 16708
5.0%
11 16033
4.8%
12 18181
5.4%
13 19956
5.9%
ValueCountFrequency (%)
23 1061
 
0.3%
22 2639
 
0.8%
21 10933
3.2%
20 16739
5.0%
19 21441
6.4%
18 21783
6.5%
17 24426
7.3%
16 23002
6.8%
15 23888
7.1%
14 21706
6.4%

minute
Real number (ℝ)

Zeros 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.2301
Minimum0
Maximum59
Zeros60696
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:07.313601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median29
Q344
95-th percentile58
Maximum59
Range59
Interquartile range (IQR)36

Descriptive statistics

Standard deviation19.300846
Coefficient of variation (CV)0.73582815
Kurtosis-1.235018
Mean26.2301
Median Absolute Deviation (MAD)16
Skewness0.092930947
Sum8833668
Variance372.52264
MonotonicityNot monotonic
2025-05-24T09:17:07.487951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60696
18.0%
30 33899
 
10.1%
45 20398
 
6.1%
15 18868
 
5.6%
55 18834
 
5.6%
59 16288
 
4.8%
10 14503
 
4.3%
25 14450
 
4.3%
5 14118
 
4.2%
29 13823
 
4.1%
Other values (50) 110899
32.9%
ValueCountFrequency (%)
0 60696
18.0%
1 2116
 
0.6%
2 848
 
0.3%
3 1439
 
0.4%
4 1357
 
0.4%
5 14118
 
4.2%
6 1381
 
0.4%
7 1092
 
0.3%
8 1695
 
0.5%
9 1445
 
0.4%
ValueCountFrequency (%)
59 16288
4.8%
58 1065
 
0.3%
57 1388
 
0.4%
56 1713
 
0.5%
55 18834
5.6%
54 1405
 
0.4%
53 1382
 
0.4%
52 1281
 
0.4%
51 1184
 
0.4%
50 12508
3.7%
Distinct6936
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2013-01-01 05:00:00
Maximum2013-12-31 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-24T09:17:07.698014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:17:07.907937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

speed
Real number (ℝ)

High correlation 

Distinct10446
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.55421
Minimum6.7690222
Maximum1976.1563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2025-05-24T09:17:08.178191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.7690222
5-th percentile272.72727
Q1354.44444
median403.17757
Q3438.42857
95-th percentile478.05195
Maximum1976.1563
Range1969.3873
Interquartile range (IQR)83.984127

Descriptive statistics

Standard deviation72.231438
Coefficient of variation (CV)0.18447366
Kurtosis12.264648
Mean391.55421
Median Absolute Deviation (MAD)40.206116
Skewness0.018411446
Sum1.3186606 × 108
Variance5217.3806
MonotonicityNot monotonic
2025-05-24T09:17:08.348062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330 827
 
0.2%
450 784
 
0.2%
300 646
 
0.2%
340 630
 
0.2%
290.5263158 553
 
0.2%
420 551
 
0.2%
280.5 542
 
0.2%
360 539
 
0.2%
298.3783784 524
 
0.2%
408 521
 
0.2%
Other values (10436) 330659
98.2%
ValueCountFrequency (%)
6.769022238 1
 
< 0.1%
31.8542223 1
 
< 0.1%
37.4287112 81
 
< 0.1%
38.22506675 9
 
< 0.1%
46.18862233 31
 
< 0.1%
56.93942235 21
 
< 0.1%
63.70844459 18
 
< 0.1%
67.2920446 21
 
< 0.1%
68.88475571 11
 
< 0.1%
73.26471128 354
0.1%
ValueCountFrequency (%)
1976.156316 6
 
< 0.1%
1029.687736 95
< 0.1%
1025.705958 3
 
< 0.1%
1022.918713 1
 
< 0.1%
1021.326002 63
< 0.1%
1003.80618 2
 
< 0.1%
985.4900023 103
< 0.1%
981.5082245 8
 
< 0.1%
977.1282689 49
< 0.1%
973.9428467 19
 
< 0.1%

month_name
Categorical

High correlation 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 MiB
Jul
29425 
Aug
29327 
Oct
28889 
Mar
28834 
May
28796 
Other values (7)
191505 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1010328
Distinct characters22
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan
2nd rowJan
3rd rowJan
4th rowJan
5th rowJan

Common Values

ValueCountFrequency (%)
Jul 29425
8.7%
Aug 29327
8.7%
Oct 28889
8.6%
Mar 28834
8.6%
May 28796
8.6%
Apr 28330
8.4%
Jun 28243
8.4%
Dec 28135
8.4%
Sep 27574
8.2%
Nov 27268
8.1%
Other values (2) 51955
15.4%

Length

2025-05-24T09:17:08.568219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jul 29425
8.7%
aug 29327
8.7%
oct 28889
8.6%
mar 28834
8.6%
may 28796
8.6%
apr 28330
8.4%
jun 28243
8.4%
dec 28135
8.4%
sep 27574
8.2%
nov 27268
8.1%
Other values (2) 51955
15.4%

Most occurring characters

ValueCountFrequency (%)
u 86995
 
8.6%
J 84672
 
8.4%
a 84634
 
8.4%
e 80660
 
8.0%
A 57657
 
5.7%
M 57630
 
5.7%
r 57164
 
5.7%
c 57024
 
5.6%
p 55904
 
5.5%
n 55247
 
5.5%
Other values (12) 332741
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 673552
66.7%
Uppercase Letter 336776
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 86995
12.9%
a 84634
12.6%
e 80660
12.0%
r 57164
8.5%
c 57024
8.5%
p 55904
8.3%
n 55247
8.2%
l 29425
 
4.4%
g 29327
 
4.4%
t 28889
 
4.3%
Other values (4) 108283
16.1%
Uppercase Letter
ValueCountFrequency (%)
J 84672
25.1%
A 57657
17.1%
M 57630
17.1%
O 28889
 
8.6%
D 28135
 
8.4%
S 27574
 
8.2%
N 27268
 
8.1%
F 24951
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1010328
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 86995
 
8.6%
J 84672
 
8.4%
a 84634
 
8.4%
e 80660
 
8.0%
A 57657
 
5.7%
M 57630
 
5.7%
r 57164
 
5.7%
c 57024
 
5.6%
p 55904
 
5.5%
n 55247
 
5.5%
Other values (12) 332741
32.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1010328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 86995
 
8.6%
J 84672
 
8.4%
a 84634
 
8.4%
e 80660
 
8.0%
A 57657
 
5.7%
M 57630
 
5.7%
r 57164
 
5.7%
c 57024
 
5.6%
p 55904
 
5.5%
n 55247
 
5.5%
Other values (12) 332741
32.9%

Interactions

2025-05-24T09:16:54.549107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:13.558456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:17.223639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:20.323255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:23.189123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:26.083654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:29.423153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:32.490124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:35.298095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:38.223100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:41.228366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:44.748325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:47.848141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:51.288354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:54.768348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:13.729558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:17.381051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:20.558328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:23.348123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:26.288043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:29.633963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:32.672342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:35.478316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:38.418166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:41.492811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:44.943300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:48.198321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:51.531806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:55.078290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:13.930272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:17.559564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:20.790511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:23.569659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:26.505184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:29.884524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:32.868390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:35.708755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:38.612976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:41.701454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:45.184322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:48.410125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:51.727874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:55.316542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:14.174580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:17.813639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:21.031247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:23.768127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:26.728114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:30.108259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:33.088203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:35.908246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:38.788064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:41.936134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:45.353270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:48.668387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:51.962145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:55.539043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:14.368001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:18.018153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:21.222049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:23.919708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:26.968171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:30.342514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:33.298083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:36.078768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:38.983234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:42.205098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:45.590998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:48.848126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:52.127887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:55.772879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:14.561913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:18.275604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:21.443060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:24.174073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:27.136426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:30.599272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:33.473140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:36.242195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:39.238339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:42.465258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:45.776211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:49.128075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:52.353122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:55.937949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:14.810590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:18.503466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:21.678224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:24.375968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:27.378204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:30.831517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:33.718144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:36.482035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:39.465541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:43.008077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:46.008152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:49.368005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:52.648139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:56.213281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:15.012852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:18.760363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:21.808465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:24.626046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:27.873294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:31.018007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:33.914001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:36.669990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:39.678290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:43.178093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:46.198347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:49.578723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:52.899085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:56.448222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:15.989611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:18.991707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.053346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:24.838359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:28.084793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:31.256730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:34.088383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:36.924504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:39.902490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:43.418063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:46.428027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:49.816239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:53.128101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:56.668073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:16.177310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:19.224306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.214542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:25.098130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:28.316830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:31.440546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:34.261058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:37.138301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:40.163088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:43.654418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:46.667858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:50.038093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:53.338082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:56.907935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:16.347119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:19.445627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.386385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:25.298127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:28.518764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:31.659564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:34.427976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:37.324892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:40.390436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:43.868216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:46.888240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:50.294106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:53.588390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:57.228066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:16.548420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:19.664182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.571882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:25.520940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:28.758207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:31.832449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:34.648133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:37.567928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:40.607954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:44.114018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:47.114974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:50.598982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:53.818251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:57.483302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:16.791379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:19.863538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.765901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:25.678395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:28.988127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:32.083531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:34.859053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:37.768156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:40.828215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:44.281505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:47.339309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:50.806168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:54.058020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:57.673174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:17.010710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:20.094481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:22.954081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:25.888344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:29.218019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:32.331080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:35.068078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:37.998271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:41.028280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:44.508381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:47.574188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:51.074536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T09:16:54.297975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-24T09:17:08.710109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
air_timearr_delayarr_timecarrierdaydep_delaydep_timedistanceflighthourminutemonthmonth_nameoriginsched_arr_timesched_dep_timespeed
air_time1.000-0.0130.0550.3550.0010.085-0.0290.962-0.462-0.0270.0330.0020.0470.2430.079-0.0250.651
arr_delay-0.0131.0000.1200.038-0.0020.6390.206-0.0780.0750.1600.022-0.0180.0400.0220.1270.161-0.247
arr_time0.0550.1201.0000.107-0.0040.1900.8070.0520.0090.7740.057-0.0030.0270.1170.8600.7760.018
carrier0.3550.0380.1071.0000.0000.0320.1070.3810.4570.1100.1060.0140.0120.5910.1350.1120.185
day0.001-0.002-0.0040.0001.0000.003-0.0000.004-0.000-0.0000.0010.0030.0370.000-0.002-0.0000.010
dep_delay0.0850.6390.1900.0320.0031.0000.2890.060-0.0100.2340.060-0.0200.0290.0190.2200.2370.034
dep_time-0.0290.2060.8070.107-0.0000.2891.000-0.0280.0330.9570.090-0.0040.0210.1110.8680.959-0.020
distance0.962-0.0780.0520.3810.0040.060-0.0281.000-0.483-0.0350.0350.0200.0210.2740.072-0.0330.792
flight-0.4620.0750.0090.457-0.000-0.0100.033-0.4831.0000.0320.0040.0040.0670.3200.0020.032-0.396
hour-0.0270.1600.7740.110-0.0000.2340.957-0.0350.0321.0000.034-0.0050.0100.0930.8800.998-0.028
minute0.0330.0220.0570.1060.0010.0600.0900.0350.0040.0341.0000.0140.0270.1250.0630.0950.037
month0.002-0.018-0.0030.0140.003-0.020-0.0040.0200.004-0.0050.0141.0001.0000.020-0.005-0.0050.067
month_name0.0470.0400.0270.0120.0370.0290.0210.0210.0670.0100.0271.0001.0000.0200.0160.0100.097
origin0.2430.0220.1170.5910.0000.0190.1110.2740.3200.0930.1250.0200.0201.0000.1370.1150.083
sched_arr_time0.0790.1270.8600.135-0.0020.2200.8680.0720.0020.8800.063-0.0050.0160.1371.0000.8820.042
sched_dep_time-0.0250.1610.7760.112-0.0000.2370.959-0.0330.0320.9980.095-0.0050.0100.1150.8821.000-0.025
speed0.651-0.2470.0180.1850.0100.034-0.0200.792-0.396-0.0280.0370.0670.0970.0830.042-0.0251.000

Missing values

2025-05-24T09:16:58.008357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T09:16:58.820338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hourspeedmonth_name
0201311517.05152.0830.081911.0UA1545N14228EWRIAH227.014005151/1/2013 5:00370.044053Jan
1201311533.05294.0850.083020.0UA1714N24211LGAIAH227.014165291/1/2013 5:00374.273128Jan
2201311542.05402.0923.085033.0AA1141N619AAJFKMIA160.010895401/1/2013 5:00408.375000Jan
3201311544.0545-1.01004.01022-18.0B6725N804JBJFKBQN183.015765451/1/2013 5:00516.721311Jan
4201311554.0600-6.0812.0837-25.0DL461N668DNLGAATL116.0762601/1/2013 6:00394.137931Jan
5201311554.0558-4.0740.072812.0UA1696N39463EWRORD150.07195581/1/2013 5:00287.600000Jan
6201311555.0600-5.0913.085419.0B6507N516JBEWRFLL158.01065601/1/2013 6:00404.430380Jan
7201311557.0600-3.0709.0723-14.0EV5708N829ASLGAIAD53.0229601/1/2013 6:00259.245283Jan
8201311557.0600-3.0838.0846-8.0B679N593JBJFKMCO140.0944601/1/2013 6:00404.571429Jan
9201311558.0600-2.0753.07458.0AA301N3ALAALGAORD138.0733601/1/2013 6:00318.695652Jan
yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hourspeedmonth_name
33676620139302240.0000002250-10.000002347.0000007-20.000000B62002N281JBJFKBUF52.00000301225030-09-2013 22:00347.307692Sep
33676720139302241.0000002246-5.000002345.0000001-16.000000B6486N346JBJFKROC47.00000264224630-09-2013 22:00337.021277Sep
33676820139302307.000000225512.000002359.00000023581.000000B6718N565JBJFKBOS33.00000187225530-09-2013 22:00340.000000Sep
33676920139302349.0000002359-10.00000325.000000350-25.000000B6745N516JBJFKPSE196.000001617235930-09-2013 23:00495.000000Sep
33677020139301349.109947184212.639071502.05499920196.895377EV5274N740EVLGABNA150.68646764184230-09-2013 18:00304.207823Sep
33677120139301349.109947145512.639071502.05499916346.8953779E3393N725MQJFKDCA150.68646213145530-09-2013 14:0084.811867Sep
33677220139301349.109947220012.639071502.05499923126.8953779E3525N725MQLGASYR150.6864619822030-09-2013 22:0078.839200Sep
33677320139301349.109947121012.639071502.05499913306.895377MQ3461N535MQLGABNA150.68646764121030-09-2013 12:00304.207823Sep
33677420139301349.109947115912.639071502.05499913446.895377MQ3572N511MQLGACLE150.68646419115930-09-2013 11:00166.836489Sep
33677520139301349.10994784012.639071502.05499910206.895377MQ3531N839MQLGARDU150.6864643184030-09-2013 08:00171.614623Sep